1. Field of the Invention
This invention relates to the method and system of predicting attribute value of unknown examples, for instance, optimum operation, etc. in the system control field and cause of fault, etc. in the knowledge processing field, by learning given examples in the field of system control, pattern recognition or knowledge processing.
2. Description of the Prior Art
A learning system is a system, where, from a plural number of examples concerning a given object system (hereinafter called the training examples), properties special to such training examples are extracted experimentally and such properties are utilized in solving the problem in an unknown example (hereinafter called the input example). For instance, in the field of system control, it is possible to collect the optimum operation in various conditions of a system as the training examples, to extract the relation between the system conditions and optimum operation, and to use such relation in solving the problem of predicting the optimum operation in unknown, specific conditions.
Further, in the field of knowledge processing, it is possible to collect symptoms and causes of the fault in the fault diagnosis system as training examples, to extract the relation between symptoms and causes, and to use such relation in solving the problem of predicting the cause in unknown, specific symptoms. In the learning system, the special relation among training examples, which is extracted from training examples and used in prediction for solving the problem of an input example will be called the decision tree. Production of a decision tree from training examples is called the learning. Therefore, the learning system can be said to be a system which learns the decision tree from training examples and uses such decision tree for solving the problem of an input example.
Heretofore, as technology for learning the decision tree from training examples there is technology called ID3. This is discussed, for instance, in the Handbook of Artificial Intelligence, Volume 3, Pitman Books Ltd. (1982), pp. 406 to 410 (hereinafter called Reference 1).
A conventional learning system using ID3 is explained by use of FIG. 3. FIG. 3 shows the learning and predicting method in the learning system, where ID3 is used.
To begin with, data used in this learning system are explained.
Training example table 2 consists of one or more of training examples 22.about.23 and classification classes 24.about.25, given correspondingly to each training example, as sown in FIG. 4. Training examples 22.about.23 are expressed in attribute descriptions 21. Each attribute description in attribute descriptions 21 consists of attribute name 211 and attribute value 212. Attribute name 211 refers to a system component element in the system control field, for instance, and indicates a name which changes variously with transition of time, for example, control valve, pressure gauge, etc.
Further, attribute value refers to the value adopted by the component element having such attribute name, and expresses the type of operation, whether opening or closing, with respect to the control valve, and the indicated value with respect to the pressure gauge. Therefore, attribute descriptions 21 can express conditions (pressure, etc.) or operation at that time (opening/closing of control valve, etc.) for the object system.
Classification classes 24.about.25 which exist corresponding to each training example in the training example table 2 refers to the classified names, when such training example is classified names when such training example is classified under certain standards. Further, the value adopted by classification class is called the class value. For instance, class value (P) is used in the case where such training example belongs to the class which shows a correct example, and class value (N) in the case where such training example belongs to the class which shows a wrong example.
Decision tree 3 is the data expressing the relation between the attribute descriptions 21, which describes training example in training Example table 2, and the class value of such training example. As shown in FIG. 5, it is expressed as a tree, having attribute names as nodes and branches which branch off corresponding to attribute values and finally arrive at the leaves corresponding to class values. Decision tree 3 expresses the class value of the training example, which is expressed by attribute descriptions 21.
Each test example 52.about.53 in test examples 5 (FIG. 7) has a data structure equal to training example 22.about.23 in test example table 2. However, classification classes are not given to test examples in advance. Test examples 5 is collection of examples, for which prediction of class value is desired. For instance, in the case of system control field mentioned previously, in order to predict whether the control valve is to be opened or closed, when the pressure gauge is at a certain value, a plural number of test examples, each of which contain the pressure gauge value and "close" is attribute values, and a plural number of test examples, each of which contain the pressure gauge value and "open" as attribute values, are prepared, and class value (P or N) of such test examples should be predicted. It can be predicted that such test examples are correct, if P, and are wrong, if N. Therefore, it is possible to obtain attribute value, for which prediction is desired, from the test examples which give class value P.
Next, the learning method and predicting method in ID3 will be explained according to FIG. 3.
In step 11, decision tree 3 is generated from attribute descriptions 22.about.23 and classification classes 24.about.25 of each training example by use of the training example table 2 (Step 11).
Method of generating this decision tree is minutely explained in Reference 1.
Decision tree 3 generated is used in predicting classification classes of each test example 52.about.53 in test examples 5A. In step 14A, classification classes of each test example 52.about.53 in test examples 5A, which had been prepared by supplementing unknown attribute value with candidates decided by the object system, are sequentially predicted by use of the decision tree 3, and correct test example is predicted from the class value (P) of the test example, and then unknown attribute to be obtained is predicted therefrom.
In the above conventional technology, the relation between the attribute descriptions 21 and the classification classes 24.about.25 was obtained from a limited number of training examples 22.about.23, and it was applied by assuming that such relation also held good in the generally unknown test examples 52.about.53. Therefore, the number of exceptions increased with increase in complexity of such relation, and the number of wrong predictions increased, thereby causing a problem of not being able to obtain unknown attribute value correctly.
Additionally, in the above conventional technology, there existed a problem of having no means of knowing the degree of certainty in prediction of unknown attribute value at the time of such prediction.